Billy Vermillion

EEG  Electroencephalography A test to measure the electrical activity of the brain. ○ Brain cells communicate by producing tiny electrical signals, called impulses.  Flat metal disks called electrodes are placed all over your scalp. Held in place with a sticky paste or specially designed helmet. Connected by wires to a recording machine.  The recording machine filters the signals into patterns that can be seen on a computer.

EEG

Brain-Computer Interface (BCI)  Current BCIs have maximum information transfer rates up to 10-25bits/min  A BCI depends on feedback and adaptation of brain activity based on that feedback  Operation depends on the interaction of two adaptive controllers: The user’s brain ○ Which produces the signals measured by the BCI The BCI itself ○ Which translates these signals in specific commands

BCI BCI2000: A General-Purpose Brain-Computer Interface (BCI) System Gerwin Schalk*, Member, IEEE, Dennis J. McFarland, Thilo Hinterberger, Niels Birbaumer, and Jonathan R.Wolpaw

P300  Infrequent or particularly significant auditory, visual, or somatosensory stimuli  Interspersed with frequent or routine stimuli  Evoke in the EEG over parietal cortex a positive peak at about 300ms latency  Only the choice desired by the user evokes a large P300 potential  Requires no initial user training P300 is a typical, or native, response to a desired choice

P300

Steady-State Visually Evoked Potential (SSVEP)  Natural responses to visual stimulation at specific frequencies  When the retina is excited by a visual stimulus ranging from 3.5 Hz to 75 Hz, the brain generates electrical activity at the same (or multiples of) frequency of the visual stimulus.  Used widely with research regarding vision  Excellent signal-to-noise ratio and relative immunity to artifacts.  SSVEP's also provide a means to characterize preferred frequencies of neocortical dynamic processes.

SSVEP fbMN1lPZE Design and Implementation of a Brain-Computer Interface With High Transfer Rates Ming Cheng*, Xiaorong Gao, Shangkai Gao, Senior Member, IEEE, and Dingfeng Xu

SSVEP

Combination

Signals 7cb3dec9973ac6a5143c37eb9efce18b-motor%20somato.jpg

fMRI 8df6cca3f243ab02d498057e053ff30b-fmri_image.jpg